1. Field of the Invention
The present invention relates to an information processor, a state judging unit, a diagnostic unit, an information processing method, a state judging method and a diagnosing method for an object which functions in a number of operation modes.
2. Description of the Related Art
In recent years, the finite resources of the earth and excessive environmental burdens have lead to great need for new ways of maintaining machines that focus on resource circulation and reduction in environmental impact so that the contemporary society is converted from expendable to sustainable.
Conventional machine maintenance adapts corrective maintenance in which a broken down machine is repaired or uniform preventive maintenance which is performed at predetermined intervals. Corrective maintenance entails a lot of time and cost for repair. Preventive maintenance generates unnecessary parts and oil wastes due to its uniformity and thereby imposes greater costs on customers. Further preventive maintenance is expensive because of the intensive labor required. There is a requirement for a departure from such conventional maintenance manners and for conversion to predictive maintenance in the future.
In predictive maintenance, the degree of soundness is diagnosed by understanding data of load and environment during operation, a database of a history of past maintenance, physical failure and others and further deterioration and remaining life are predicted in order to find a defect on a machine at an early stage and provide a safe operation environment.
For example, Japanese Patent Application Laid-Open (KOKAI) No. 2002-323013 (hereinafter, referred to as patent reference 1) relates to an abnormality diagnostic unit for a working vehicle such as a construction machine; a pressure sensor for detecting discharge pressure from a hydraulic pump, an engine speed sensor for detecting engine speed, an oil temperature sensor for detecting the oil temperature in a hydraulic circuit and a communication device for radio transmitting detection data by these sensors to a network management center (a network station) are installed in a vehicle body of a working machine (a hydraulic excavator) and a monitoring station (e.g., an office of the manager of the working machine) obtains the detection data of the working machine from the network station through the Internet and diagnoses any abnormalities of the working machine based on the detection data.
Further, Japanese Patent Application Laid-Open (KOKAI) No. HEI 11-338848 (hereinafter, referred to as patent reference 2) relates to an abnormality detection unit for a fixed machinery facility such as a batch plant or a continuous plant; normal data when the object plant is in a normal state is previously collected, on the basis of the normal data, characteristics of the normal data are extracted using a Self-Organizing Map; on the basis of the characteristics, a characteristic map indicating distance relationships between outputting units are created and stored as a normal state model, and an abnormality of the object plant is detected based on the normal state model and input data (input vectors) Here, the normal state model is formed by converting multi-dimensional data into a visualized two-dimensional map as shown in FIG. 13 (in which the multi-dimensional data is classified into five clusters expressed by regions with symbols R1-R5), and if the input data has a characteristic identical to the normal state model, the input data is judged to be normal data. The technique of patent reference 2 can totally detect an abnormality of multi-dimensional input data in real time.
A construction machine such as a hydraulic excavator mentioned above has multi-dimensional parameters (detection factors) of working pressure to control the machine body moving forward and backward and slewing, working pressure of a bucket cylinder to control the bucket, working pressure of a stick cylinder to control the stick, and working pressure of the boom cylinder to control the boom in addition to engine speed, discharge pressure from a hydraulic pump and oil temperature in a hydraulic circuit.
A construction machine carries out an operation series by combining a number of working operations (i.e., working modes). For example, an operation series whereby piled earth and sand are loaded onto the vessel (container) of a truck can be roughly divided into four working modes (operation modes) of “an operation from the beginning to the end of shoveling earth and sand with the bucket (working mode 1)”, “operation of slewing the machine body to move the bucket loaded with earth and sand to the point over the vessel of the truck after shoveling earth and sand (working mode 2)”, “operation from opening the bucket to transfer earth and sand to the vessel to completing the transfer (working mode 3)” and “operation from returning the bucket to the piled earth and sand to being ready for operation mode 1 (working mode 4)”.
Namely, each parameter value varies with operation mode but analysis of each individual parameter value frequently cannot result in precise abnormal diagnosis. For example, although each individual parameter value is within a normal range the current working operation may not totally correspond to any one of the above four operation modes (in macro view). In this case, the working operation is presumed to be in an unknown operation mode or to have something wrong.
For diagnosing a machine, whether or not the current working operation conforms with one of the operation modes previously classified is judged and, if the current working operation conforms with no operation mode, the machine is judged to be in an operation mode other than the above operation modes or to have something wrong, so that it seems that the abnormality in the machine can be found more rapidly. For this reason, if all the possible operation modes of a machine of a diagnosing object are precisely recognized in advance, an operation mode corresponding to the current working operation can be judged in real time based on multi-dimension parameter values.
Considering the conventional technique from this viewpoint, using the Self-Organizing Map of patent reference 2 can classify each operation mode of the machine even if a parameter is multi-dimensional.
However, if a machine has a large number of operation modes, clusters substantially identical in quantity to the operation modes are formed in a single two-dimensional Self-Organizing Map, so that further increasing in quantity of operation modes reduces the area of each cluster and overlaps between adjacent clusters is intensified to make the boundaries less clear. Such a two-dimensional map can be visually classified, but classification requires human judgment that may not be precise. Further, if a new operation mode is to be added, the Self-Organizing Map has to be recreated from the beginning whereupon diagnosing the machine may take much longer.
The description so far has used the example of a construction machine but the diagnostic unit can also be applied to many diagnosing objects (objects) whose operations (or variation of parameters) can be classified into a number of operation modes (or variation modes).
With the foregoing problems in view, the object of the present invention is to provide an information processor, a state judging unit, a diagnostic unit, an information processing method, a state judging method and a diagnosing method for precisely recognizing each operation carried out by an object, such as a machine, that functions in a number of operation modes.